pymc-labs/pymc-marketing
Jan 2026 – Apr 2026
4 Months active
Languages Used
PythonTOMLYAML
Technical Skills
Jupyter NotebookPython programmingdata analysisdata validationdata visualizationschema design
isofer
PROFILE
Isofer
Contributed to the pymc-labs/pymc-marketing repository by building core analytics and infrastructure for marketing mix modeling workflows. Developed robust data validation, schema integrity, and serialization frameworks using Python, enabling reliable data handling and cross-component interoperability. Introduced interactive Plotly visualizations and cost-per-unit analysis, supporting dynamic exploration and budget optimization for both monetary and non-monetary channels. Enhanced model flexibility with Bayesian inference and statistical modeling, while expanding test coverage and CI/CD reliability. Delivered modular plotting suites and improved documentation, ensuring transparency and ease of adoption. Addressed critical bugs and stabilized infrastructure, resulting in more dependable, production-ready marketing analytics pipelines and tools.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
26Total
Bugs
3
Commits
26
Features
9
Lines of code
58,845
Activity Months4
Your Network
18 peopleWork History
April 2026
2 Commits • 2 Features
Apr 1, 2026In April 2026, delivered foundational capabilities for pymc-marketing analytics by introducing a robust serialization framework and initiating plotting capabilities, while stabilizing core paths and addressing critical import issues. These efforts reduce serialization fragility, enable cross-component interoperability, and lay the groundwork for faster, more reliable deployments in marketing analytics workflows.
2 Commits • 2 Features
Apr 1, 2026In April 2026, delivered foundational capabilities for pymc-marketing analytics by introducing a robust serialization framework and initiating plotting capabilities, while stabilizing core paths and addressing critical import issues. These efforts reduce serialization fragility, enable cross-component interoperability, and lay the groundwork for faster, more reliable deployments in marketing analytics workflows.
April 2026
March 2026
5 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for pymc-marketing: Delivered end-to-end Cost-per-unit (CPU) functionality across the MMM framework for channels with non-monetary data (e.g., impressions, clicks), including data validation, serialization, and seamless integration into budget optimization. Enhanced plotting with weighted-average cost calculations and released a demo notebook showing CPU analysis with interactive plots and a budget-optimization workflow. Introduced CovFunc enum to choose covariance functions in the HSGP model, along with input validation and updated documentation. Corrected MMM formulas and refreshed documentation to clarify parameters and improve accuracy. Overall, these efforts expand model coverage, enable cost-aware budgeting for non-monetary channels, and provide a ready-to-demo experience for stakeholders.
March 2026
5 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for pymc-marketing: Delivered end-to-end Cost-per-unit (CPU) functionality across the MMM framework for channels with non-monetary data (e.g., impressions, clicks), including data validation, serialization, and seamless integration into budget optimization. Enhanced plotting with weighted-average cost calculations and released a demo notebook showing CPU analysis with interactive plots and a budget-optimization workflow. Introduced CovFunc enum to choose covariance functions in the HSGP model, along with input validation and updated documentation. Corrected MMM formulas and refreshed documentation to clarify parameters and improve accuracy. Overall, these efforts expand model coverage, enable cost-aware budgeting for non-monetary channels, and provide a ready-to-demo experience for stakeholders.
February 2026
14 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for pymc-marketing: delivered interactive MMM Plotly visualizations, launched Incrementality framework enhancements, improved data integrity and infrastructure, and shipped a notebook showcasing interactive plotting. These contributions enable faster, data-driven marketing optimization with richer exploration and more reliable data pipelines. Highlights include Phase 1 and Phase 2 interactive MMM visualizations (contributions, ROAS, saturation, adstock with faceting and HDI bands), new Incrementality module for incremental contributions and CAC/ROAS improvements with expanded test coverage, robust MMM data wrapper/schema handling and CI/workflow improvements, and a notebook illustrating interactive plotting capabilities.
14 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for pymc-marketing: delivered interactive MMM Plotly visualizations, launched Incrementality framework enhancements, improved data integrity and infrastructure, and shipped a notebook showcasing interactive plotting. These contributions enable faster, data-driven marketing optimization with richer exploration and more reliable data pipelines. Highlights include Phase 1 and Phase 2 interactive MMM visualizations (contributions, ROAS, saturation, adstock with faceting and HDI bands), new Incrementality module for incremental contributions and CAC/ROAS improvements with expanded test coverage, robust MMM data wrapper/schema handling and CI/workflow improvements, and a notebook illustrating interactive plotting capabilities.
February 2026
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 (Month: 2026-01) – Focused on strengthening data integrity, expanding model introspection, and enabling actionable visuals for MMM workflows. Delivered three major features with robust validation, fixed critical data handling glitches, and established improved test coverage to drive reliability in production marketing analytics.
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 (Month: 2026-01) – Focused on strengthening data integrity, expanding model introspection, and enabling actionable visuals for MMM workflows. Delivered three major features with robust validation, fixed critical data handling glitches, and established improved test coverage to drive reliability in production marketing analytics.
Activity
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Quality Metrics
Correctness84.6%
Maintainability82.4%
Architecture81.6%
Performance82.4%
AI Usage43.0%
Skills & Technologies
Programming Languages
PythonTOMLYAML
Technical Skills
API developmentBayesian inferenceCI/CDDevOpsGitHub ActionsImage ProcessingJupyter NotebookLoggingPandasPlotlyPolarsPythonPython package managementPython programmingbackend development
Repositories Contributed To
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